{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "610694b1-f4bd-4f23-8822-e762d912ca4c",
   "metadata": {},
   "source": [
    "卷积神经网络"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7585160-697d-40cd-9f4e-278dfcf9e45f",
   "metadata": {},
   "source": [
    "多层感知机主要是将图像中的像素逐行展开，得到长度为784的向量，并输入进全连接层中但是该方法具有一定的局限性。1、图像在同一列邻近的像素在这个向量中相距较远，它们构成的模式难以被模型识别。2、对于大尺寸的输入图像，使用全连接层容易造成模型过大。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae99676a-f5e0-4f1b-b2c3-8330ef3ca3e3",
   "metadata": {},
   "source": [
    "卷积层尝试解决这个问题。1、卷积层保留输入形状，使图像的像素在高和宽两个方向上的相关性均可能被有效识别。2、卷积层通过滑动窗口将同一卷积核与不同位置的输入重复计算，从而避免参数尺寸过大"
   ]
  },
  {
   "attachments": {
    "8565c007-3eec-4590-8375-98fc1f87b1af.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "6b7005b9-9d32-488a-9004-acc9bbbcffe0",
   "metadata": {},
   "source": [
    "卷积神经网络就是含卷积层的网络。LeNet展示了通过梯度下降训练\n",
    "卷积神经⽹络可以达到⼿写数字识别在当时最先进的结果。\n",
    "![image.png](attachment:8565c007-3eec-4590-8375-98fc1f87b1af.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91e4472c-7180-4b6a-996b-e5ee7d37d32d",
   "metadata": {},
   "source": [
    "卷积层块里的基本单位是卷积层后接最大池化层：卷积层⽤来识别图像⾥的空间模式，如线条和物体局部，之后的最⼤池化层则⽤来降低卷积层对位置的敏感性。第⼀个卷积层\r\n",
    "输出通道数为6，第⼆个卷积层输出通道数则增加到16。这是因为第⼆个卷积层⽐第⼀个卷积层的输\r\n",
    "的⾼和宽要⼩，所以增加输出通道使两个卷积层的参数尺⼨类似。卷积层块的两个最⼤池化层的窗形\r\n",
    "2×2均为 ，且步幅为2。由于池化窗⼝与步幅形状相同，池化窗⼝在输⼊上每次滑动所覆盖互不重叠不᯿叠。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffccf12a-051a-47c3-83aa-8bb9c86a4728",
   "metadata": {},
   "source": [
    "当卷积层块的输出传⼊全连接层块时，全连接层块会\n",
    "将⼩批量中每个样本变平（flatten）。也就是说，全连接层的输⼊形状将变成⼆维，其中第⼀维是⼩批\n",
    "量中的样本，第⼆维是每个样本变平后的向量表示，且向量⻓度为通道、⾼和宽的乘积。全连接层块含\n",
    "3个全连接层。它们的输出个数分别是120、84和10，其中10为输出的类别个数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bea77c14-5d70-43a8-9146-ebb3a5fb5af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"..\") \n",
    "import d2lzh_pytorch as d2l\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "class LeNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LeNet, self).__init__()\n",
    "        self.conv = nn.Sequential(\n",
    "            nn.Conv2d(1,6,5),   #二维卷积层输入通道、输出通道、卷积核大小\n",
    "            nn.Sigmoid(),   #激活函数\n",
    "            nn.MaxPool2d(2,2),   #卷积核、步幅\n",
    "            nn.Conv2d(6,16,5),   #第二个卷积层\n",
    "            nn.Sigmoid(),\n",
    "            nn.MaxPool2d(2,2)\n",
    "        )\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(16*4*4,120),\n",
    "            nn.Sigmoid(),\n",
    "            nn.Linear(120,84),\n",
    "            nn.Sigmoid(),\n",
    "            nn.Linear(84,10)\n",
    "        )\n",
    "    def forward(self,img):\n",
    "        feature = self.conv(img)\n",
    "        output = self.fc(feature.view(img.shape[0],-1))\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4ac0f15-295f-465b-83c0-57fae657494d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LeNet(\n",
      "  (conv): Sequential(\n",
      "    (0): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
      "    (1): Sigmoid()\n",
      "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
      "    (4): Sigmoid()\n",
      "    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (fc): Sequential(\n",
      "    (0): Linear(in_features=256, out_features=120, bias=True)\n",
      "    (1): Sigmoid()\n",
      "    (2): Linear(in_features=120, out_features=84, bias=True)\n",
      "    (3): Sigmoid()\n",
      "    (4): Linear(in_features=84, out_features=10, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "net = LeNet()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8e797165-20ec-4b1a-8d69-fb6f863de252",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "train_iter,test_iter = d2l.load_data_fashion_mnist(batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b11aacd4-3c21-4438-a593-1ebe012272a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training on cuda\n",
      "epoch 1, loss 0.0327, train acc 0.989, test acc 0.919, time 7.7 sec\n",
      "epoch 2, loss 0.0353, train acc 0.989, test acc 0.920, time 8.3 sec\n",
      "epoch 3, loss 0.0294, train acc 0.990, test acc 0.921, time 8.3 sec\n",
      "epoch 4, loss 0.0354, train acc 0.988, test acc 0.920, time 8.2 sec\n",
      "epoch 5, loss 0.0316, train acc 0.990, test acc 0.919, time 8.2 sec\n",
      "epoch 6, loss 0.0340, train acc 0.988, test acc 0.919, time 8.3 sec\n",
      "epoch 7, loss 0.0304, train acc 0.990, test acc 0.920, time 8.3 sec\n",
      "epoch 8, loss 0.0380, train acc 0.988, test acc 0.920, time 8.2 sec\n",
      "epoch 9, loss 0.0322, train acc 0.989, test acc 0.919, time 8.4 sec\n",
      "epoch 10, loss 0.0318, train acc 0.990, test acc 0.920, time 7.8 sec\n",
      "epoch 11, loss 0.0304, train acc 0.990, test acc 0.920, time 7.9 sec\n",
      "epoch 12, loss 0.0304, train acc 0.990, test acc 0.919, time 7.8 sec\n",
      "epoch 13, loss 0.0313, train acc 0.990, test acc 0.921, time 7.8 sec\n",
      "epoch 14, loss 0.0298, train acc 0.990, test acc 0.922, time 7.9 sec\n",
      "epoch 15, loss 0.0302, train acc 0.990, test acc 0.920, time 8.0 sec\n",
      "epoch 16, loss 0.0287, train acc 0.991, test acc 0.916, time 8.3 sec\n",
      "epoch 17, loss 0.0298, train acc 0.991, test acc 0.917, time 8.4 sec\n",
      "epoch 18, loss 0.0285, train acc 0.991, test acc 0.919, time 8.3 sec\n",
      "epoch 19, loss 0.0295, train acc 0.991, test acc 0.919, time 8.2 sec\n",
      "epoch 20, loss 0.0281, train acc 0.991, test acc 0.918, time 8.2 sec\n",
      "epoch 21, loss 0.0251, train acc 0.992, test acc 0.919, time 8.2 sec\n",
      "epoch 22, loss 0.0313, train acc 0.990, test acc 0.919, time 8.4 sec\n",
      "epoch 23, loss 0.0254, train acc 0.992, test acc 0.918, time 8.4 sec\n",
      "epoch 24, loss 0.0241, train acc 0.993, test acc 0.918, time 8.4 sec\n",
      "epoch 25, loss 0.0282, train acc 0.991, test acc 0.920, time 8.4 sec\n",
      "epoch 26, loss 0.0296, train acc 0.991, test acc 0.919, time 8.3 sec\n",
      "epoch 27, loss 0.0274, train acc 0.991, test acc 0.918, time 8.3 sec\n",
      "epoch 28, loss 0.0269, train acc 0.991, test acc 0.919, time 8.4 sec\n",
      "epoch 29, loss 0.0255, train acc 0.991, test acc 0.919, time 8.3 sec\n",
      "epoch 30, loss 0.0287, train acc 0.991, test acc 0.919, time 8.3 sec\n",
      "epoch 31, loss 0.0248, train acc 0.992, test acc 0.920, time 8.3 sec\n",
      "epoch 32, loss 0.0228, train acc 0.993, test acc 0.919, time 8.3 sec\n",
      "epoch 33, loss 0.0274, train acc 0.991, test acc 0.916, time 8.3 sec\n",
      "epoch 34, loss 0.0264, train acc 0.992, test acc 0.917, time 7.6 sec\n",
      "epoch 35, loss 0.0247, train acc 0.992, test acc 0.920, time 7.8 sec\n",
      "epoch 36, loss 0.0218, train acc 0.993, test acc 0.922, time 8.3 sec\n",
      "epoch 37, loss 0.0239, train acc 0.993, test acc 0.920, time 8.3 sec\n",
      "epoch 38, loss 0.0240, train acc 0.993, test acc 0.920, time 7.6 sec\n",
      "epoch 39, loss 0.0264, train acc 0.992, test acc 0.919, time 7.0 sec\n",
      "epoch 40, loss 0.0245, train acc 0.992, test acc 0.918, time 7.8 sec\n",
      "epoch 41, loss 0.0227, train acc 0.993, test acc 0.918, time 7.8 sec\n",
      "epoch 42, loss 0.0225, train acc 0.993, test acc 0.920, time 7.8 sec\n",
      "epoch 43, loss 0.0206, train acc 0.993, test acc 0.919, time 7.7 sec\n",
      "epoch 44, loss 0.0241, train acc 0.993, test acc 0.919, time 7.6 sec\n",
      "epoch 45, loss 0.0270, train acc 0.992, test acc 0.916, time 7.6 sec\n",
      "epoch 46, loss 0.0249, train acc 0.992, test acc 0.914, time 7.6 sec\n",
      "epoch 47, loss 0.0225, train acc 0.993, test acc 0.919, time 7.7 sec\n",
      "epoch 48, loss 0.0229, train acc 0.993, test acc 0.920, time 7.8 sec\n",
      "epoch 49, loss 0.0254, train acc 0.993, test acc 0.919, time 7.8 sec\n",
      "epoch 50, loss 0.0219, train acc 0.994, test acc 0.919, time 7.7 sec\n"
     ]
    }
   ],
   "source": [
    "lr, num_epochs = 0.001, 50\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
    "d2l.train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "078c2698-4dc4-4d3b-ae78-03c3b0defd21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on cuda\n",
      "epoch 1, loss 0.5659, train acc 0.798, test acc 0.875, time 7.2 sec\n",
      "epoch 2, loss 0.3463, train acc 0.879, test acc 0.895, time 8.2 sec\n",
      "epoch 3, loss 0.2987, train acc 0.897, test acc 0.900, time 7.8 sec\n",
      "epoch 4, loss 0.2723, train acc 0.904, test acc 0.901, time 8.1 sec\n",
      "epoch 5, loss 0.2590, train acc 0.908, test acc 0.908, time 8.3 sec\n",
      "epoch 6, loss 0.2423, train acc 0.916, test acc 0.909, time 8.3 sec\n",
      "epoch 7, loss 0.2280, train acc 0.920, test acc 0.901, time 8.3 sec\n",
      "epoch 8, loss 0.2155, train acc 0.924, test acc 0.912, time 8.3 sec\n",
      "epoch 9, loss 0.2133, train acc 0.926, test acc 0.910, time 8.4 sec\n",
      "epoch 10, loss 0.2039, train acc 0.928, test acc 0.911, time 8.3 sec\n",
      "epoch 11, loss 0.1507, train acc 0.945, test acc 0.916, time 8.3 sec\n",
      "epoch 12, loss 0.1358, train acc 0.951, test acc 0.917, time 8.2 sec\n",
      "epoch 13, loss 0.1258, train acc 0.954, test acc 0.919, time 8.2 sec\n",
      "epoch 14, loss 0.1163, train acc 0.959, test acc 0.920, time 8.3 sec\n",
      "epoch 15, loss 0.1093, train acc 0.960, test acc 0.916, time 8.3 sec\n",
      "epoch 16, loss 0.1073, train acc 0.962, test acc 0.919, time 8.3 sec\n",
      "epoch 17, loss 0.0997, train acc 0.964, test acc 0.919, time 8.3 sec\n",
      "epoch 18, loss 0.0945, train acc 0.966, test acc 0.921, time 8.3 sec\n",
      "epoch 19, loss 0.0875, train acc 0.968, test acc 0.921, time 8.3 sec\n",
      "epoch 20, loss 0.0839, train acc 0.970, test acc 0.918, time 8.4 sec\n",
      "epoch 21, loss 0.0637, train acc 0.978, test acc 0.921, time 8.3 sec\n",
      "epoch 22, loss 0.0512, train acc 0.982, test acc 0.921, time 8.2 sec\n",
      "epoch 23, loss 0.0495, train acc 0.982, test acc 0.923, time 8.3 sec\n",
      "epoch 24, loss 0.0469, train acc 0.983, test acc 0.919, time 8.3 sec\n",
      "epoch 25, loss 0.0445, train acc 0.984, test acc 0.920, time 8.1 sec\n",
      "epoch 26, loss 0.0434, train acc 0.985, test acc 0.920, time 7.3 sec\n",
      "epoch 27, loss 0.0393, train acc 0.986, test acc 0.921, time 8.3 sec\n",
      "epoch 28, loss 0.0384, train acc 0.986, test acc 0.922, time 8.3 sec\n",
      "epoch 29, loss 0.0347, train acc 0.988, test acc 0.920, time 7.8 sec\n",
      "epoch 30, loss 0.0364, train acc 0.987, test acc 0.919, time 7.9 sec\n",
      "epoch 31, loss 0.0290, train acc 0.990, test acc 0.922, time 7.9 sec\n",
      "epoch 32, loss 0.0237, train acc 0.992, test acc 0.923, time 7.6 sec\n",
      "epoch 33, loss 0.0223, train acc 0.992, test acc 0.922, time 7.8 sec\n",
      "epoch 34, loss 0.0239, train acc 0.992, test acc 0.922, time 7.5 sec\n",
      "epoch 35, loss 0.0222, train acc 0.992, test acc 0.922, time 8.2 sec\n",
      "epoch 36, loss 0.0212, train acc 0.993, test acc 0.922, time 8.2 sec\n",
      "epoch 37, loss 0.0185, train acc 0.994, test acc 0.919, time 7.8 sec\n",
      "epoch 38, loss 0.0195, train acc 0.994, test acc 0.920, time 7.9 sec\n",
      "epoch 39, loss 0.0189, train acc 0.993, test acc 0.921, time 8.3 sec\n",
      "epoch 40, loss 0.0176, train acc 0.994, test acc 0.920, time 8.3 sec\n",
      "epoch 41, loss 0.0154, train acc 0.994, test acc 0.923, time 8.2 sec\n",
      "epoch 42, loss 0.0132, train acc 0.996, test acc 0.921, time 7.7 sec\n",
      "epoch 43, loss 0.0147, train acc 0.995, test acc 0.922, time 7.9 sec\n",
      "epoch 44, loss 0.0142, train acc 0.995, test acc 0.923, time 7.9 sec\n",
      "epoch 45, loss 0.0126, train acc 0.996, test acc 0.923, time 7.9 sec\n",
      "epoch 46, loss 0.0149, train acc 0.995, test acc 0.921, time 8.3 sec\n",
      "epoch 47, loss 0.0130, train acc 0.996, test acc 0.921, time 8.3 sec\n",
      "epoch 48, loss 0.0124, train acc 0.996, test acc 0.921, time 8.3 sec\n",
      "epoch 49, loss 0.0116, train acc 0.996, test acc 0.922, time 8.3 sec\n",
      "epoch 50, loss 0.0118, train acc 0.996, test acc 0.923, time 8.4 sec\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"..\")\n",
    "import d2lzh_pytorch as d2l\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "class LeNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LeNet, self).__init__()\n",
    "        self.conv = nn.Sequential(\n",
    "            nn.Conv2d(1, 32, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2, 2),\n",
    "\n",
    "            nn.Conv2d(32, 64, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2, 2),\n",
    "\n",
    "            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(128 * 7 * 7, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(256, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(128, 10)\n",
    "        )\n",
    "\n",
    "    def forward(self, img):\n",
    "        feature = self.conv(img)\n",
    "        output = self.fc(feature.view(img.shape[0], -1))\n",
    "        return output\n",
    "\n",
    "def train_ch5(net, train_iter, test_iter, optimizer, device, num_epochs):\n",
    "    net.to(device)\n",
    "    print(\"Training on\", device)\n",
    "    loss = nn.CrossEntropyLoss()\n",
    "    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        train_l_sum, train_acc_sum, n, batch_count = 0.0, 0.0, 0, 0\n",
    "        start = time.time()\n",
    "\n",
    "        for X, y in train_iter:\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            y_hat = net(X)\n",
    "            l = loss(y_hat, y)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            l.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            train_l_sum += l.cpu().item()\n",
    "            train_acc_sum += (y_hat.argmax(dim=1) == y).sum().cpu().item()\n",
    "            n += y.shape[0]\n",
    "            batch_count += 1\n",
    "\n",
    "        scheduler.step()\n",
    "        test_acc = evaluate_accuracy(test_iter, net)\n",
    "\n",
    "        print(f'epoch {epoch+1}, loss {train_l_sum/batch_count:.4f}, '\n",
    "              f'train acc {train_acc_sum/n:.3f}, test acc {test_acc:.3f}, '\n",
    "              f'time {time.time()-start:.1f} sec')\n",
    "\n",
    "def evaluate_accuracy(data_iter, net, device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')):\n",
    "    \"\"\"\n",
    "    计算模型在数据集上的准确率。\n",
    "    :param data_iter: 数据迭代器\n",
    "    :param net: 神经网络模型\n",
    "    :param device: 设备（CPU或GPU）\n",
    "    :return: 准确率\n",
    "    \"\"\"\n",
    "    acc_sum, n = 0.0, 0\n",
    "    with torch.no_grad():\n",
    "        for X, y in data_iter:\n",
    "            if isinstance(net, torch.nn.Module):\n",
    "                net.eval()  # 评估模式, 关闭 dropout\n",
    "                acc_sum += (net(X.to(device)).argmax(dim=1) == y.to(device)).float().sum().cpu().item()\n",
    "                net.train()  # 改回训练模式\n",
    "            else:  # 自定义的模型, 3.13 节之后不会用到, 不考虑 GPU\n",
    "                if 'is_training' in net.__code__.co_varnames:  # 如果有 is_training 这个参数\n",
    "                    acc_sum += (net(X, is_training=False).argmax(dim=1) == y).float().sum().item()\n",
    "                else:\n",
    "                    acc_sum += (net(X).argmax(dim=1) == y).float().sum().item()\n",
    "            n += y.shape[0]\n",
    "    return acc_sum / n\n",
    "\n",
    "\n",
    "batch_size = 128\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=batch_size)\n",
    "lr, num_epochs = 0.003, 50\n",
    "net = LeNet()\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
    "train_ch5(net, train_iter, test_iter, optimizer, device, num_epochs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f685adfa-60aa-43d1-8e81-9ae61094db00",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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